Data augmentation is a well-established approach for learning models with built-in invariance to
noise and/or robustness to data perturbations. In addition it is also used when we have additional
prior knowledge on the instance structures to which the models should be invariant. In imaging problems
such structures include translations, rotations, scalings,etc, [1]}, [2]}.
